{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Round Trip Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When evaluating the performance of an investing strategy, it is helpful to quantify the frequency, duration, and profitability of its independent bets, or \"round trip\" trades. A round trip trade is started when a new long or short position is opened and then later completely or partially closed out.\n",
    "\n",
    "The intent of the round trip tearsheet is to help differentiate strategies that profited off a few lucky trades from strategies that profited repeatedly from genuine alpha. Breaking down round trip profitability by traded name and sector can also help inform universe selection and identify exposure risks. For example, even if your equity curve looks robust, if only two securities in your universe of fifteen names contributed to overall profitability, you may have reason to question the logic of your strategy.\n",
    "\n",
    "To identify round trips, pyfolio reconstructs the complete portfolio based on the transactions that you pass in. When you make a trade, pyfolio checks if shares are already present in your portfolio purchased at a certain price. If there are, we compute the PnL, returns and duration of that round trip trade. In calculating round trips, pyfolio will also append position closing transactions at the last timestamp in the positions data. This closing transaction will cause the PnL from any open positions to realized as completed round trips."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-07T02:43:48.874652Z",
     "start_time": "2021-09-07T02:43:48.864891Z"
    }
   },
   "outputs": [],
   "source": [
    "# silence warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-07T02:43:50.546591Z",
     "start_time": "2021-09-07T02:43:48.989776Z"
    }
   },
   "outputs": [],
   "source": [
    "import pyfolio as pf\n",
    "%matplotlib inline\n",
    "import gzip\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-07T02:43:51.697971Z",
     "start_time": "2021-09-07T02:43:51.616535Z"
    }
   },
   "outputs": [],
   "source": [
    "transactions = pd.read_csv(gzip.open('../tests/test_data/test_txn.csv.gz'),\n",
    "                    index_col=0, parse_dates=True)\n",
    "positions = pd.read_csv(gzip.open('../tests/test_data/test_pos.csv.gz'),\n",
    "                    index_col=0, parse_dates=True)\n",
    "returns = pd.read_csv(gzip.open('../tests/test_data/test_returns.csv.gz'),\n",
    "                      index_col=0, parse_dates=True, header=None)[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Sector Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-07T02:43:52.439793Z",
     "start_time": "2021-09-07T02:43:52.437219Z"
    }
   },
   "outputs": [],
   "source": [
    "# Optional: Sector mappings may be passed in as a dict or pd.Series. If a mapping is\n",
    "# provided, PnL from symbols with mappings will be summed to display profitability by sector.\n",
    "sect_map = {'COST': 'Consumer Goods', 'INTC':'Technology', 'CERN':'Healthcare', 'GPS':'Technology',\n",
    "            'MMM': 'Construction', 'DELL': 'Technology', 'AMD':'Technology'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Round Trip Tear Sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The easiest way to run the analysis is to call `pyfolio.create_round_trip_tear_sheet()`. Passing in a sector map is optional. You can also pass `round_trips=True` to `pyfolio.create_full_tear_sheet()` to have this be created along all the other analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-07T02:44:23.168045Z",
     "start_time": "2021-09-07T02:43:54.942707Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Summary stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total number of round_trips</th>\n",
       "      <td>5822.00</td>\n",
       "      <td>1155.00</td>\n",
       "      <td>4667.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent profitable</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winning round_trips</th>\n",
       "      <td>2886.00</td>\n",
       "      <td>595.00</td>\n",
       "      <td>2291.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Losing round_trips</th>\n",
       "      <td>2917.00</td>\n",
       "      <td>553.00</td>\n",
       "      <td>2364.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Even round_trips</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PnL stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total profit</th>\n",
       "      <td>$65404.25</td>\n",
       "      <td>$3560.10</td>\n",
       "      <td>$61844.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross profit</th>\n",
       "      <td>$448803.34</td>\n",
       "      <td>$20608.45</td>\n",
       "      <td>$428194.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross loss</th>\n",
       "      <td>$-383399.09</td>\n",
       "      <td>$-17048.35</td>\n",
       "      <td>$-366350.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit factor</th>\n",
       "      <td>$1.17</td>\n",
       "      <td>$1.21</td>\n",
       "      <td>$1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. trade net profit</th>\n",
       "      <td>$11.23</td>\n",
       "      <td>$3.08</td>\n",
       "      <td>$13.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. winning trade</th>\n",
       "      <td>$155.51</td>\n",
       "      <td>$34.64</td>\n",
       "      <td>$186.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. losing trade</th>\n",
       "      <td>$-131.44</td>\n",
       "      <td>$-30.83</td>\n",
       "      <td>$-154.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratio Avg. Win:Avg. Loss</th>\n",
       "      <td>$1.18</td>\n",
       "      <td>$1.12</td>\n",
       "      <td>$1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest winning trade</th>\n",
       "      <td>$9500.14</td>\n",
       "      <td>$1623.24</td>\n",
       "      <td>$9500.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest losing trade</th>\n",
       "      <td>$-22902.83</td>\n",
       "      <td>$-661.29</td>\n",
       "      <td>$-22902.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Duration stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg duration</th>\n",
       "      <td>13 days 03:21:49.653555479</td>\n",
       "      <td>2 days 10:39:35.064935064</td>\n",
       "      <td>15 days 18:53:36.628026569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median duration</th>\n",
       "      <td>8 days 00:00:00</td>\n",
       "      <td>2 days 00:00:00</td>\n",
       "      <td>12 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longest duration</th>\n",
       "      <td>84 days 00:00:00</td>\n",
       "      <td>13 days 00:00:00</td>\n",
       "      <td>84 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shortest duration</th>\n",
       "      <td>0 days 00:00:01</td>\n",
       "      <td>1 days 00:00:00</td>\n",
       "      <td>0 days 00:00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Return stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg returns all round_trips</th>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns winning</th>\n",
       "      <td>0.13%</td>\n",
       "      <td>0.13%</td>\n",
       "      <td>0.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns losing</th>\n",
       "      <td>-0.11%</td>\n",
       "      <td>-0.12%</td>\n",
       "      <td>-0.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns all round_trips</th>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns winning</th>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns losing</th>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest winning trade</th>\n",
       "      <td>6.78%</td>\n",
       "      <td>6.78%</td>\n",
       "      <td>6.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest losing trade</th>\n",
       "      <td>-17.23%</td>\n",
       "      <td>-3.95%</td>\n",
       "      <td>-17.23%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbol stats</th>\n",
       "      <th>AMD</th>\n",
       "      <th>CERN</th>\n",
       "      <th>COST</th>\n",
       "      <th>DELL</th>\n",
       "      <th>GPS</th>\n",
       "      <th>INTC</th>\n",
       "      <th>MMM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg returns all round_trips</th>\n",
       "      <td>0.04%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>-0.04%</td>\n",
       "      <td>0.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns winning</th>\n",
       "      <td>0.13%</td>\n",
       "      <td>0.08%</td>\n",
       "      <td>0.09%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>0.17%</td>\n",
       "      <td>0.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns losing</th>\n",
       "      <td>-0.07%</td>\n",
       "      <td>-0.08%</td>\n",
       "      <td>-0.10%</td>\n",
       "      <td>-0.08%</td>\n",
       "      <td>-0.18%</td>\n",
       "      <td>-0.21%</td>\n",
       "      <td>-0.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns all round_trips</th>\n",
       "      <td>0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns winning</th>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns losing</th>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>-0.03%</td>\n",
       "      <td>-0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest winning trade</th>\n",
       "      <td>6.78%</td>\n",
       "      <td>1.69%</td>\n",
       "      <td>1.48%</td>\n",
       "      <td>3.29%</td>\n",
       "      <td>2.72%</td>\n",
       "      <td>5.01%</td>\n",
       "      <td>6.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest losing trade</th>\n",
       "      <td>-1.59%</td>\n",
       "      <td>-6.39%</td>\n",
       "      <td>-4.48%</td>\n",
       "      <td>-3.28%</td>\n",
       "      <td>-17.23%</td>\n",
       "      <td>-6.86%</td>\n",
       "      <td>-1.60%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Profitability (PnL / PnL total) per name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COST</th>\n",
       "      <td>39.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTC</th>\n",
       "      <td>38.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CERN</th>\n",
       "      <td>32.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>22.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPS</th>\n",
       "      <td>4.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMD</th>\n",
       "      <td>-6.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>-31.15%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Profitability (PnL / PnL total) per name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Consumer Goods</th>\n",
       "      <td>39.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>32.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Construction</th>\n",
       "      <td>22.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>5.65%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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LiwE8CwLK2qp4BbK4uFhpPXmZvC112iwqKoKRkVGVy80eP34sbqtqbNXZu3ev5O/x48fDy8sLq1atwujRo2FgYCC2X1V/K46puLi4ygdxNG3aVGxL/r+GhoYKY5Mv18/Ly6v12NTthzbGpqxN+esrjq0q8vcfNWqUZMLn9ddfR58+fTBr1izExMQgNDRU0k9dHavaUtV+l+8fXS/FdHV1haurq/j3sGHD4O7uDh8fH6xbtw6fffZZjftRvh1QfbzqtFlXdH2MqdomkVxdnB9UR5UYqg+0uZ9UjZ36rDb7T9XY3hjU9zhfn6hyzqGvmFzXI3/88QcCAwNVqpucnIzWrVtLytLT0xESEgI7Ozt88sknkm3NmjUD8OwnAiorKSkRt8vrKqsnL5PXVadNExMT7N69G7t371Y6nrfeeqvasanL1NQUvr6++Oyzz3D9+nXY2tqKX25V9ReQjk1ZPXldeVvy/42KikJUVJTS+j4+PuL/V3ds6vZDG2NTVvfq1asApGOrSnVjfu2119CtWzckJyeLAVjXx6q2NGvWTOlJiyaTStpmb2+PAQMG4PTp0wDU/2xUGa8uPhtdH2Oqtkkkp+n5gbqUxVB9oM39pGrs1Gd1tf+UxfbGQB/jfH1S+ZxDXzG5rke6deuGyMhIlepWfvjGv//+izfeeAPm5ubYtGkTTE1NJdvlS1KULVnJycmR/DxAVUsx5WXy16vbppmZGaZPny6p98cffyAuLg6hoaFiG+o+gKUq8p+EyM3NBfDsoSUmJiZKlzLn5OTAwMBAHJOVlRXKysrw6NEjyRKw0tJSPHr0SOyrhYUFjI2N0adPH4wZM0bS5rFjx5CUlISPP/5YvCKl7tjU6Yc2xlZVm/KrGBXHVpWaxtyhQwfcvn1bMmZ5v3VxrGpLVX2V719lS8nqgw4dOoiBruJ+rKzyvlV1vOq0WVd0fYyp2iaRnCbnB7VVOYbqA23uJ1Vjpz6ry/1XObY3Bvoa5+uTiucc+orJdT1iZWWFCRMmqP26hw8fIjg4GKWlpYiJiUHbtm0V6vTo0QMAcPHiRcnvyWVnZyMrK0vcDjybOTp37hwEQZAsBbtw4QJMTU3F32BVp00HBwecO3cO48ePl7R57tw5mJqa4o033qjz30PMzMwEAHFm1dDQEDKZDBcvXlSoe+HCBXTt2hXNmzdXGNvgwYPFehcvXkR5ebm43dDQEHZ2digqKlL47L777jtYW1vD19e31mNQpx/aGFtVbV67dk3jscllZmZKfo9d18eqttjb22PXrl0oKCiQPOzk/Pnz4vb6KDMzU3w4okwmQ5MmTXDx4kWMGDFCrFNaWorU1FTJBJOq41Wnzbqi62NM1TaJ5Gp7fqCJyjFUH2hzP6kaO/VZXe6/yrG9MdDXOF+fVDzn0Fd8oJmeKywsxKxZs5CdnY1Nmzaha9euSut1794d3bp1w759+/D06VOxfM+ePTA0NJSc1Hp4eODu3btITEwUyx48eIAff/wRw4YNE5NgbbRZG8oemPHw4UPs3r0bL774IqytrcXykSNHIiUlRfJUz+vXr+P06dPw8PAQy/r37w8LCwuFZex79uyBqakpXn31VbXbrA1t9ENXY1P2OR05cgQ3b96UnKjo+ljVFg8PD5SVleHAgQNiWWlpKeLi4uDq6or27dtrvQ/VUfb5nD17FmfOnBE/H3NzcwwYMACHDx9GQUGBWO/w4cMoLCyUHBOqjledNuuKro8xbX4fEqlLnRjamKkTOxsTVWN7Y1Df43x9oso5h74yEARB0HUnqPbmzZuHxMRE+Pj4oF+/fpJtbdu2xaBBg8S/k5KSMHfuXPTv3x+enp5IS0tDbGwspkyZgo8++kis9/TpU/j7++Pq1asIDg6GpaUl9uzZg3///RdxcXGSBF4bbco9fvwYu3btAgCkpKTg559/RnBwMMzNzdGxY0eMGzcOABAdHY3ExEQMGTIEHTt2RHZ2Nvbt24cHDx5g/fr1GDp0qNhmfn4+xo8fj6KiIsyYMQNGRkbYsWMHBEHAN998I5kti42NxbJly+Dh4YHBgwfj7Nmz+Oabb/Dee+9h5syZtWpT3bFpqx/aHFtVRowYAQcHB7z88sswMzPDhQsX8M0336BLly44ePAgzM3Nxbq6PFa16Z133kFiYiKmT5+OLl26ID4+HhcvXkRMTAzc3NyeSx+qEhgYiObNm8PFxQWWlpa4evUq9u3bB3Nzcxw8eBAdO3YEAFy6dAm+vr7o3r07Jk2ahKysLGzfvh39+vXD5s2bJW2qOl512lTFhg0bADx7DsV3330HHx8fvPjii2jZsiWmTZsGQP++D4nUpY0Y2hBpIx43JurE9sagPsf5+kTVcw59xORaz7m7u1d5T0vfvn3FgCF34sQJrFu3Dunp6WjdujV8fHwwb948NGkivUMgNzcXK1euxIkTJ1BSUoJevXohPDxcstxRm20CwK1btzBs2LAax/brr79i69atSEtLQ25uLkxNTeHs7IzZs2cr/SLLysrC8uXL8dtvv6G8vBz9+vXDokWL0LlzZ4W6+/fvx7Zt23Dr1i106NABAQEBSh/2oU6b6oxNm/3Q1tiqsnr1avz000+4ffs2iouL0a5dO7i7uyMkJEThp00A3R6r2lJSUoI1a9bgyJEjyM3NhZ2dHcLCwjBw4MDn8v7V2blzp3i1IT8/H61bt8bgwYPx1ltvKQS5s2fPIioqCpcvX4aZmRk8PT0RFham8KwHdcarapuqsLOzU1reqVMnnDx5Uvxbn74PidSlrRja0GgrHjcW6sb2hq4+x/n6RJ1zDn3D5JqIiIiIiIhIQ7znmoiIiIiIiEhDTK6JiIiIiIiINMTkmoiIiIiIiEhDTK6JiIiIiIiINMTkmoiIiIiIiEhDTK6JiIiIiIiINMTkmoiIiIiIiEhDTK6JqFaio6NhZ2eHW7duNYj3ed5u3boFOzs7REdH12m7Z86cgZ2dHeLi4uq03cqio6O1/h5ERPoqLi4OdnZ2OHPmTIN4H/p/AQEBcHd313U3qJ5ick1aIT/Br/jPxcUFEyZMQExMDJ4+fVqrduVB5Mcff6zjHpM25eXlITo6ul4F/1u3biE6Ohqpqam67goRUaNX1XnD+PHjsWPHDjx58kTXXdSq6OhonDhxQtfd0IodO3ZwMlYFN2/exBtvvAFXV1cMGzYMMTExSutFRERg7NixDf6/CX3VRNcdoIbNy8sLr776KgRBwN27dxEfH4/ly5fj2rVr+OSTT3TdPXpO8vLysG7dOoSEhKBfv35qvXbu3LmYNWsWTExM6rRPt2/fxrp169CpUyf06NGjTtvWpT59+uDChQto0kS7X+9PnjxBaWmpVt+DiBqfiucN9+7dw+HDhxEZGYn09PQGfd6wbt06jB8/HsOHD1frdd7e3hg9ejSMjY211DPN7dy5E506dcKECRN03ZU6sXXr1jpvs7y8HPPnz0dJSQneffddXLt2DcuXL8cLL7yAkSNHivXOnz+PvXv3Yvfu3VqP81Q7/FRIq15++WV4e3uLf/v7+2PUqFE4cOAA3nnnHbRt21aHvdO+/Px8mJmZaf01DZF8PzRp0qReBBBBEFBYWIgWLVrouivVMjQ0RNOmTbXS9u3bt7Fy5Ur88ssvKCwsBPBsBl0mkyE0NBSvvPKKVt6XiBqP6s4bQkND0bp1ax32rv6Qx0gjIyMYGRk99/etL8rKylBeXq61uKdMXU/2A8CNGzeQlpaGnTt3ihchrl69imPHjonJdVlZGRYvXgx/f384OjrWeR+obnBZOD1XZmZmcHFxgSAIyMzMBAC4u7sjICAA6enpmDVrFlxcXODm5oa3334bOTk5dfr+8vtkMjMzMXfuXLi5ucHV1RXz588X+1ORIAjYvXs3JkyYACcnJ7i4uCAgIACnT5+W1Kt4/+z333+PCRMmwNHREREREdX2Rz72y5cv44033oCbmxvGjh0rbv/jjz8wY8YMuLm5wdHREePHj8eBAweqbKcyZfffypfWJycnY+vWrRg+fDh69uyJkSNHIj4+XqGN8vJyfPXVV3B3d0evXr3g5eWFb7/9ttpxVe7DsGHDADybmZcv95Pfr1TTvlN2z7W87OrVq4iIiMCgQYPg6OiISZMmITk5ucY+xcXFITAwEACwcOFCsU/yfVhxv8XGxsLT0xO9evXCtm3bAAAXLlxAeHg4Ro4cKR4Xvr6+OH78uNL3O3v2LHx9feHo6IiBAwdi2bJlYnJamarHXFWUfeYVyw4dOoTRo0ejZ8+eGDp0KDZv3qxSu0+fPsWcOXOQlJSE6dOnw8vLCwEBAfjwww/RuXNn3Lx5U6V2iIjUYWpqCicnJwiCoPA9c+XKFcyfPx/9+vVDr1694Onpic2bNyvcelbVPbLKnn1Rm+/L/fv3w8PDAz179sTrr7+OHTt2QBAElcYn7wMAxMfHS5bFy9nZ2SE8PBzJycnw8/ODi4sL5s6dC0D5PdfyslOnTiE6OhpDhw5Fz549MWbMGCQkJKjVr+rOa06dOoXg4GD07t0bvXr1wpgxY7Bnzx5JO3Z2drh9+zZ+//13ydjkMV0+tsqUjati7I+MjMSrr74KR0dHpKSkqH1u89NPP2HatGno168fHB0dMWTIEISEhCAjI6PGfaPseJKXZWdnIywsDH369IGTkxPeeOMNldosKSkBALRq1Uosa9WqFYqKisS/t2zZgoKCAvznP/+psT3SHd1fDqJGRRAE/PPPPwAAS0tLsTw7OxuBgYEYPnw4PvjgA1y5cgX79u1Dfn6+mNDUlcLCQgQEBMDR0RFhYWH4559/sHv3bpw/fx7x8fGwsrIS677//vtISEjAyJEjMWHCBJSWluLIkSMIDg5GdHS0mDTKnThxArt27YKfnx98fX1Vmt29c+cOpk+fDg8PD4wYMUJMuk6ePImQkBC0bdsWM2bMgJmZGRISErB48WLcunULoaGhGu2H1atXo7i4GFOmTIGJiQn27NmD8PBwdOnSBW5ubmK9yMhI7Ny5E3369EFQUBDu37+PZcuWoXPnziq9j62tLRYuXIjIyEi8/vrreP311wFA4QpwbfbdggULYGhoiJkzZyI/Px/79u3Dm2++ic2bN2PgwIFVvq5Pnz6YM2cONm7ciClTpojjrbySIiYmBo8ePcKkSZNgZWWFF154AQBw/PhxXL9+HR4eHujUqRMePXqE+Ph4hISEICoqCmPGjBHbOH/+PGbMmIEWLVpg5syZMDc3x/fff48FCxYo7Zu6x5w69u7di3v37mHixIlo2bIlvv32W0RFReGFF16Q9FmZjIwMpKWl4a233kJISAiio6PFZX7+/v617hMRUU3kk98VE4///d//RUBAAJo0aYKpU6eibdu2SEpKQlRUFK5cuYLPPvtMo/dU9ftyx44diIyMhL29PcLCwlBUVIRt27ahTZs2Kr1P69atsXLlSnzwwQfo3bs3Jk+erLTexYsXcfToUUyePBnjx49Xqe2oqCgUFhbCz88PwLOENSwsDCUlJSov0a4qNu/btw9Lly6Fs7Mz5syZg+bNm+PUqVP46KOPcPPmTTHGrVy5EpGRkbC0tMScOXMk466t9957D82aNUNwcDAAwMrKCrdv3wag2rnN77//jrlz56J79+6YPXs2zM3NcffuXSQnJ+PmzZuwsbGpVb8KCwsxbdo0ODk5ITQ0FLdu3cLOnTsxb948fPfdd9WuMLCxsYGFhQU2bNiA999/H+np6fif//kfvPXWWwCexeAvv/wS0dHRMDU1rVX/6DkRiLTg9OnTgkwmE6Kjo4X79+8L9+/fF1JTU4VFixYJMplMmDx5slh36NChgkwmExISEiRtfPTRR4JMJhPS09PFskOHDgkymUz44YcfatWvadOmCTKZTIiIiJCUHzt2TJDJZMKSJUsUyvbu3SupW1ZWJowfP14YOnSoUF5eLgiCIGRmZgoymUx4+eWXhWvXrqncH/nY9+/fLyl/8uSJMGTIEMHNzU3IysoSy0tKSoQpU6YI9vb2QkZGhqSdadOmKbQv/xwOHToklsn3obe3t1BSUiKWZ2VlCQ4ODkJoaKhYlp6eLtjZ2QmBgYHCkydPxPKLFy8KdnZ2gkwmEzIzM2scp3z/rF27tsptVe27tWvXKryPvGzixImSMfz777+Cs7Oz4OHhUWOflO2bytv69Okj3Lt3T2F7QUGBQllhYaEwYsQIYdSoUZLyKVOmCA4ODsL169fFspKSEsHHx0dhn6hzzKkzLnnZoEGDhLy8PEmf+/XrJ/nvsSo3btwQZDKZsHLlSkEQnn0GyvYdEVFtKDtvuHLlinguMHHiREn9KVOmCD169BBSU1PFsvLycuHtt98WZDKZcOrUKbF82rRpwtChQxXeU1lsUuf7Mjc3V3BychJGjRolFBYWiuXyWCSTyYTTp0+rNH6ZTCYsWLCgym0ymUz47bffFLbJY3rF95GXDRkyRDKGvLw8YciQIUKfPn2EoqKiavtTXWzOzs4WevbsKYSFhSm87pNPPhHs7e2FmzdvimVVnaNUN25l45LH/mnTpgllZWVK66tybrN8+XJBJpMpje+qUHY8yc8vN23aJCnfvHmzIJPJhF9++aXGdo8fPy64urqKn3dwcLBQXFwslJeXC9OmTVO6v6n+4bJw0qro6GgMGDAAAwYMgLe3Nw4dOgR3d3esX79eUq9du3bw9PSUlPXv3x8AxCvddWnWrFmSv19//XXY2NggMTFRLPv222/RokULDB8+HA8ePBD/5eXlwd3dHbdv38aNGzck7bz22muwtbVVqy8WFhYKM8iXLl3CnTt34OPjg/bt24vlJiYmePPNN1FeXi7pa234+/tL7htq3749bGxsJGNKTEyEIAiYMWOGZMbVwcEBgwYN0uj9K6vNvgsKCpKMQX5F4fr160hPT9e4T97e3kqvPlScNS4qKsLDhw9RVFSE/v37Iz09Hfn5+QCA+/fv49y5c3B3d5fMhJuYmCAoKEih3docc+rw8fGBubm5+Hfz5s3h7OysUptdunTBK6+8gu3btyMsLAwpKSnIzs7mQ82IqE5VPG8YO3Ysdu/ejREjRmDDhg1inYrfrfb29mK5gYGBuFy6qtt0VKXK9+Wvv/6KoqIiTJ06Fc2bNxfLVVkNpC57e/tqV2Qp4+fnJxmDubk5fH19kZubq/KvdyiLzUePHkVpaSkmTpwoiVUPHjyAu7s7ysvLcerUKbX6qo7p06dX+SwWVc5t5Pvk6NGjdfrEbUNDQ/GWMzl1zmWHDx+On3/+GQcOHMCxY8ewdetWNG3aFAcPHkRaWho+/PBDFBcXIyIiAu7u7hg9ejS+/vrrOus/1Q0uCyetmjJlCjw8PGBgYIDmzZvD2toaFhYWCvWULTGW13v06FGd9qlly5aSpd9ytra2OHHiBAoLC2Fqaor09HQUFBRUG8zu378vSZqsra3V7k/nzp0VlgrJ70V66aWXFOp3794dAJTeI67u+1ZmYWEhLq2q+B7dunVTqGtra4tff/1V/Lu4uBiPHz+W1DE3N0ezZs1U6k9t9p2yZFxelpmZqXayrmqf7t+/jzVr1iAxMRH3799X2J6XlwczM7Nq95+yz7Y2x5w6XnzxRYUyCwsLlf4bMzAwwLp16xATE4OEhARcvXoVv/76KzZu3IgRI0bggw8+UPrfFRGROuTnDWVlZUhLS8OWLVuQlZUleWBVdTGyW7duMDQ01DhGqvJ9Ke9HVTGyotLSUuTm5krKTE1NVX5IZm1iZHX9qvgcE3XfVz55rWySWO7evXsqtV8b1e0LVc5tpk6disTERHz88ceIioqCm5sbXnnlFXh5eWm0XL1du3YKD1ZT91zWzMxM8rCynJwcrFy5EgsXLkSbNm2wdOlS/Pbbb1ixYgWys7Px4YcfonXr1goXqEh3mFyTVnXt2lWlmdbq7kMRVHwoSF0TBAGtW7eu9r4teaIrV3HmWlW1eY2qqvs9cUPDul248v3332PhwoWSssjISJXv69LmfqgtZX0SBAHBwcFIT09HYGAgevbsCXNzcxgZGeHQoUP47rvvUF5eXqv3q80xpw5NnyjbrFkzzJ49G7Nnz0Z0dDQKCwthZGSEHTt24Nq1azh48OBzfWotETU8Fc8bXnvtNbi5ucHf3x9Lly7F6tWr6/S9qouRdf1ddu7cOYWrmiEhIeI9tTXRVYysKg4CwIoVK9CuXTulr1P1uSxVqe6zqW7SXpVzG0tLSxw8eBBnz57FqVOn8McffyAyMhLR0dHYtGkTXFxcatVnbZzLRkREwMHBARMmTEB5eTni4+OxZMkS9OnTB8CzB7MdOnSIyXU9wuSaGp28vDzk5OQoXGVLT09HmzZtxCW/Xbt2xY0bN+Dk5PTcf35JPmN+7do1hW3ysoqBq6qrj3V1dfv69evo0qWLZFvlZdeDBw/G9u3bJWXyqwoGBgYa9aMq6enpkiWBFftVU2CvbZ/+/vtv8Qm1b7/9tmRb5Se5yz/H69evK7Sj7LPV5TFXG927d8eECRNgYmKC9evXIyMjQ+mVJCKi2nJ1dYW3tze++eYbBAQEwNXVtdoYef36dZSXlyvEyEuXLinUraur29evX8eAAQMk2yrHSHt7e4UYqWkCWhNlsUfeL2VX5lUlv3JsaWmp9lL1yrR1/lITIyMj9OvXT/zZqytXrsDHxwdffvklNm3apNX3VtXJkyfx008/4ciRIwCAhw8foqSkRHy4KgB06NABly9f1lUXSQnec02NUuUvzuPHjyMjIwPDhw8Xy8aNG4fy8nJ8/vnnStvQ5pInBwcHdOzYEXFxcZKfIysrK8PWrVthYGAgeWq0tbU1MjIykJ2dLZaVlpYiNjZWo364u7vDwMAA27dvl8wiX7p0SeF+qnbt2mHgwIGSf/IZbfmEReUlcZrasWOH5J7frKwsHDlyBDY2NjUuCa9tn+Sz4pVnodPS0hTu8Wvbti2cnZ1x8uRJyU9xlJaWYseOHQpt6/KYq0l+fj7KysqUbpPfs/Y8f2eUiBqPefPmwcjICGvXrgUAtGnTBi4uLkhKSkJaWppYTxAEMb7Lf5kCeBYjCwoKcOHCBbGsvLxc6fewOgYNGoRmzZohNjZW8pNJ8lhUUatWrRRiZMXk2tTUtM5vg9uzZ4/kdq3Hjx9j7969aNmyJfr27VvrdkeNGgUTExNER0ejuLhYYfvjx48lsblFixZVjs3a2hopKSmS/Zebmyv5Ocm69uDBA4Wybt26oWnTpnV+nlJb+fn5+PjjjzF//nzx4oaFhQWMjY0lx3xaWlqVqwdIN3jlmvTSsWPHlM7IdunSBV5eXtW+1tLSEsePH8fdu3fRt29f8ae42rZti5CQELGeh4cHJkyYgK+//hqXLl3C0KFDYWlpiaysLKSkpOCff/7R+KFiVTEyMsKSJUsQEhKCiRMnYvLkyWjRogV++OEHpKSkYM6cOZJ7jqZOnYqEhAQEBQXB19cXZWVlOHz4sMbLyGxtbTF16lR8/fXXmD59OkaMGIH79+8jNjYW9vb2Ks+WWlpaomvXrkhISEDnzp3Rtm1bNG/eXOnvjqrj6dOnmDp1KkaPHo2CggLs3bsXJSUlWLx4cY2vfemll9CiRQvs3r0bzZo1Q8uWLdG6dWuFqw+V2draonv37tiyZQuKi4thY2ODjIwM7Nu3DzKZTOHqSHh4OAICAuDn54epU6eKP8WlbMmbLo+5mly6dAkLFizA2LFj4eTkhDt37iA3NxdXr17Frl270LdvX61fhSGixqlr167w9PTEkSNHcPbsWfTu3RuLFi1CQEAApk6dCn9/f1hZWSEpKQm//vorvLy8JN/lkydPxvbt2zF//nwEBgbC2NgYR48erXbpsSpatWqFd955BytWrICvry/GjRuHoqIi7N27F9bW1mpdUXR2dkZycjI2bdqEjh07wsDAAKNHj9aof5aWlpg0aZJ4e1ZcXBzu3LmDiIgIjc4PXnjhBXz00UdYvHgxPD09MXbsWHTq1AkPHjxAWloaTpw4gYSEBPHquJOTEw4ePIg1a9bA1tYWhoaGGDp0KExNTTF16lS8//77mD59Ory9vZGXl4cDBw6gY8eOkosLdWnJkiXIysrC4MGD0bFjRxQXF+OHH35AQUEBvL29tfKe6lq9ejUsLCzEnxsDnp0benl5YcOGDRAEAXfv3sXPP/+MyMhIHfaUKmNyTXopISFBafngwYNrTK5NTU0RExOD5cuX47PPPoMgCHjllVcQHh6uMPsXGRmJfv36Yf/+/fjqq69QVlYGKysrvPzyy3j33XfrbDzKuLu7Y8eOHfjyyy+xdetWlJWVwdbWFhEREZg0aZKkrpubGz799FNs3LgRq1atQrt27eDn54eePXtW+8ARVSxatAht27bF/v37sXLlSlhbW+O///0v/vnnH7VOHKKiorB8+XKsXr0aRUVF6NSpk8bJ9YoVK7B3715s3rwZeXl5sLOzw6effqrSk8ybNWuG1atXY82aNVi+fDlKS0vRt2/fGpNrIyMjfPXVV1ixYgXi4+NRVFSE7t27Y8WKFbhy5YpCcu3i4oLt27fjs88+w6ZNm2Bubo6RI0fCz89P6dNkdXnMVadHjx6YNm0akpKSEBcXh/v378PIyAgdOnTAtGnTxCf0EhFpw9y5c5GQkIAvvvgCu3btQq9evbB3716sXbsWe/bsQWFhITp37oz33ntPkpAAz5Zfr1+/Hp9//jm++OILWFhYwNvbGz4+Phg1apRG/QoODoapqan4Pd+hQwcEBwfD3NwcH374ocrtLF26FMuWLcPGjRtRUFAAABon1++99x7Onj2L3bt34969e7CxsUFUVFSdPMncx8cH1tbW2LZtG/bt24fHjx/DwsICNjY2eOeddyS33oWGhiI3Nxe7d+9GXl4eBEFAYmIiTE1NMXbsWNy9exexsbGIjIxE586dMW/ePBgaGuL8+fMa91MZb29vxMXFIT4+Hg8ePICZmRleeuklrF27FiNHjtTKe6ojJSUF+/btw549exSeii6/eLB582Y0b94coaGhGDdunA56SVUxEHT1tCgiHQgICMDt27dx8uRJXXeFNBAdHY1169YhMTFRo/vGqPaio6PRqVMnlR9YR0REz0dcXBwWLlyInTt3ivcUE9HzwXuuiYiIiIiIiDTEZeFERKS2vn37omXLlrruBhEREVG9weSaiIjUxqWGRERERFK855qIiIiIiIhIQ7znmoiIiIiIiEhDTK6JiIiIiIiINMTkmoiIiIiIiEhDTK6JiIiIiIiINMTkmoiIiIiIiEhDTK6JiIiIiIiINPR/P+s2a6zFsOIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x1296 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pf.create_round_trip_tear_sheet(returns, positions, transactions, sector_mappings=sect_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore underlying functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the hood, several functions are being called. `extract_round_trips()` does the portfolio reconstruction and creates the round-trip trades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-07T02:44:28.339232Z",
     "start_time": "2021-09-07T02:44:23.171046Z"
    }
   },
   "outputs": [],
   "source": [
    "rts = pf.round_trips.extract_round_trips(transactions, \n",
    "                                         portfolio_value=positions.sum(axis='columns') / (returns + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-07T02:44:28.358198Z",
     "start_time": "2021-09-07T02:44:28.340552Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pnl</th>\n",
       "      <th>open_dt</th>\n",
       "      <th>close_dt</th>\n",
       "      <th>long</th>\n",
       "      <th>rt_returns</th>\n",
       "      <th>symbol</th>\n",
       "      <th>duration</th>\n",
       "      <th>returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-126.000000</td>\n",
       "      <td>2004-01-09 00:00:00+00:00</td>\n",
       "      <td>2004-01-13 00:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.022523</td>\n",
       "      <td>AMD</td>\n",
       "      <td>4 days</td>\n",
       "      <td>0.000793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.020000</td>\n",
       "      <td>2004-01-09 00:00:00+00:00</td>\n",
       "      <td>2004-01-16 00:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>0.078507</td>\n",
       "      <td>AMD</td>\n",
       "      <td>7 days</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1540.099065</td>\n",
       "      <td>2004-01-09 00:00:00+00:00</td>\n",
       "      <td>2004-01-20 00:00:00+00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>0.104696</td>\n",
       "      <td>AMD</td>\n",
       "      <td>11 days</td>\n",
       "      <td>0.000184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>287.119806</td>\n",
       "      <td>2004-01-20 00:00:00+00:00</td>\n",
       "      <td>2004-01-21 00:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.085155</td>\n",
       "      <td>AMD</td>\n",
       "      <td>1 days</td>\n",
       "      <td>0.000984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103.349947</td>\n",
       "      <td>2004-01-20 00:00:00+00:00</td>\n",
       "      <td>2004-01-22 00:00:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.112198</td>\n",
       "      <td>AMD</td>\n",
       "      <td>2 days</td>\n",
       "      <td>-0.001249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pnl                   open_dt                  close_dt   long  \\\n",
       "0  -126.000000 2004-01-09 00:00:00+00:00 2004-01-13 00:00:00+00:00   True   \n",
       "1    50.020000 2004-01-09 00:00:00+00:00 2004-01-16 00:00:00+00:00   True   \n",
       "2  1540.099065 2004-01-09 00:00:00+00:00 2004-01-20 00:00:00+00:00   True   \n",
       "3   287.119806 2004-01-20 00:00:00+00:00 2004-01-21 00:00:00+00:00  False   \n",
       "4   103.349947 2004-01-20 00:00:00+00:00 2004-01-22 00:00:00+00:00  False   \n",
       "\n",
       "   rt_returns symbol duration   returns  \n",
       "0   -0.022523    AMD   4 days  0.000793  \n",
       "1    0.078507    AMD   7 days  0.000000  \n",
       "2    0.104696    AMD  11 days  0.000184  \n",
       "3    0.085155    AMD   1 days  0.000984  \n",
       "4    0.112198    AMD   2 days -0.001249  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-07T02:44:28.443008Z",
     "start_time": "2021-09-07T02:44:28.359105Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Summary stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total number of round_trips</th>\n",
       "      <td>5819.00</td>\n",
       "      <td>1155.00</td>\n",
       "      <td>4664.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent profitable</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winning round_trips</th>\n",
       "      <td>2887.00</td>\n",
       "      <td>595.00</td>\n",
       "      <td>2292.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Losing round_trips</th>\n",
       "      <td>2914.00</td>\n",
       "      <td>553.00</td>\n",
       "      <td>2361.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Even round_trips</th>\n",
       "      <td>18.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PnL stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total profit</th>\n",
       "      <td>$67003.94</td>\n",
       "      <td>$3531.32</td>\n",
       "      <td>$63472.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross profit</th>\n",
       "      <td>$448674.42</td>\n",
       "      <td>$20579.67</td>\n",
       "      <td>$428094.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross loss</th>\n",
       "      <td>$-381670.48</td>\n",
       "      <td>$-17048.35</td>\n",
       "      <td>$-364622.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit factor</th>\n",
       "      <td>$1.18</td>\n",
       "      <td>$1.21</td>\n",
       "      <td>$1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. trade net profit</th>\n",
       "      <td>$11.51</td>\n",
       "      <td>$3.06</td>\n",
       "      <td>$13.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. winning trade</th>\n",
       "      <td>$155.41</td>\n",
       "      <td>$34.59</td>\n",
       "      <td>$186.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg. losing trade</th>\n",
       "      <td>$-130.98</td>\n",
       "      <td>$-30.83</td>\n",
       "      <td>$-154.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratio Avg. Win:Avg. Loss</th>\n",
       "      <td>$1.19</td>\n",
       "      <td>$1.12</td>\n",
       "      <td>$1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest winning trade</th>\n",
       "      <td>$9500.14</td>\n",
       "      <td>$1623.24</td>\n",
       "      <td>$9500.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest losing trade</th>\n",
       "      <td>$-22902.83</td>\n",
       "      <td>$-661.29</td>\n",
       "      <td>$-22902.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Duration stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg duration</th>\n",
       "      <td>13 days 03:27:07.702354356</td>\n",
       "      <td>2 days 10:39:35.064935064</td>\n",
       "      <td>15 days 19:02:40.548885077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median duration</th>\n",
       "      <td>8 days 00:00:00</td>\n",
       "      <td>2 days 00:00:00</td>\n",
       "      <td>12 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longest duration</th>\n",
       "      <td>84 days 00:00:00</td>\n",
       "      <td>13 days 00:00:00</td>\n",
       "      <td>84 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shortest duration</th>\n",
       "      <td>1 days 00:00:00</td>\n",
       "      <td>1 days 00:00:00</td>\n",
       "      <td>1 days 00:00:00</td>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Return stats</th>\n",
       "      <th>All trades</th>\n",
       "      <th>Short trades</th>\n",
       "      <th>Long trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg returns all round_trips</th>\n",
       "      <td>0.01%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns winning</th>\n",
       "      <td>0.13%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>0.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns losing</th>\n",
       "      <td>-0.11%</td>\n",
       "      <td>-0.11%</td>\n",
       "      <td>-0.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns all round_trips</th>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns winning</th>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns losing</th>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>-0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest winning trade</th>\n",
       "      <td>6.78%</td>\n",
       "      <td>6.78%</td>\n",
       "      <td>6.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest losing trade</th>\n",
       "      <td>-17.23%</td>\n",
       "      <td>-3.92%</td>\n",
       "      <td>-17.23%</td>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbol stats</th>\n",
       "      <th>AMD</th>\n",
       "      <th>CERN</th>\n",
       "      <th>COST</th>\n",
       "      <th>DELL</th>\n",
       "      <th>GPS</th>\n",
       "      <th>INTC</th>\n",
       "      <th>MMM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg returns all round_trips</th>\n",
       "      <td>0.04%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>-0.04%</td>\n",
       "      <td>0.08%</td>\n",
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       "    <tr>\n",
       "      <th>Avg returns winning</th>\n",
       "      <td>0.13%</td>\n",
       "      <td>0.08%</td>\n",
       "      <td>0.09%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>0.17%</td>\n",
       "      <td>0.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg returns losing</th>\n",
       "      <td>-0.07%</td>\n",
       "      <td>-0.08%</td>\n",
       "      <td>-0.10%</td>\n",
       "      <td>-0.08%</td>\n",
       "      <td>-0.18%</td>\n",
       "      <td>-0.21%</td>\n",
       "      <td>-0.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns all round_trips</th>\n",
       "      <td>0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>-0.00%</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns winning</th>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>0.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median returns losing</th>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>-0.03%</td>\n",
       "      <td>-0.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest winning trade</th>\n",
       "      <td>6.78%</td>\n",
       "      <td>1.69%</td>\n",
       "      <td>1.48%</td>\n",
       "      <td>3.29%</td>\n",
       "      <td>2.72%</td>\n",
       "      <td>5.01%</td>\n",
       "      <td>6.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Largest losing trade</th>\n",
       "      <td>-1.59%</td>\n",
       "      <td>-6.39%</td>\n",
       "      <td>-4.48%</td>\n",
       "      <td>-3.28%</td>\n",
       "      <td>-17.23%</td>\n",
       "      <td>-6.86%</td>\n",
       "      <td>-1.60%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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   "source": [
    "pf.round_trips.print_round_trip_stats(rts)"
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  }
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